Analysis date: 2023-10-18

Depends on

DIPG_FirstBatch_DataProcessing Script

load("../Data/Cache/Xenografts_Batch1_2_DataProcessing.RData")

TODO

  • Do differential abudance analysis for prep batch and mass spec run

Setup

Load libraries and functions

PTMsigDB

PtmSigdb <- readxl::read_excel("../../../General/Code/Data/db_ptm.sig.db.all.v2.0.0/data_PTMsigDB_all_sites_v2.0.0.xlsx")
## Warning: Expecting numeric in D66110 / R66110C4: got '2407414-sm;d'
## Warning: Expecting numeric in D66117 / R66117C4: got '3755605-sm;d'
## Warning: Expecting numeric in D66118 / R66118C4: got '3829801-sm;d'
## Warning: Expecting numeric in D66251 / R66251C4: got '2134717-sm;u'
## Warning: Expecting numeric in D66252 / R66252C4: got '2134718-sm;u'
## Warning: Expecting numeric in D66508 / R66508C4: got '1668802-me;u'
## Warning: Expecting numeric in D66525 / R66525C4: got '1668801-me;u'
## Warning: Expecting numeric in D66592 / R66592C4: got '2295400-sm;u'
## Warning: Expecting numeric in D66650 / R66650C4: got '60885600-pa;d'
## Warning: Expecting numeric in D66651 / R66651C4: got '60885601-pa;d'
## Warning: Expecting numeric in D66652 / R66652C4: got '8545503-sm;u'
## Warning: Expecting numeric in D66653 / R66653C4: got '8545504-sm;u'
## Warning: Expecting numeric in D66654 / R66654C4: got '8545505-sm;u'
## Warning: Expecting numeric in D66748 / R66748C4: got '31089521-sm;d'
## Warning: Expecting numeric in D66819 / R66819C4: got '1668802-me;d'
## Warning: Expecting numeric in D67030 / R67030C4: got '50455000-sm;u'
## Warning: Expecting numeric in D67031 / R67031C4: got '50455001-sm;u'
## Warning: Expecting numeric in D67032 / R67032C4: got '50455002-sm;u'
## Warning: Expecting numeric in D67043 / R67043C4: got '31094520-me;u'
## Warning: Expecting numeric in D67056 / R67056C4: got '27581511-ne;d'
## Warning: Expecting numeric in D67057 / R67057C4: got '27581513-ne;d'
## Warning: Expecting numeric in D67080 / R67080C4: got '2535901-sm;d'
## Warning: Expecting numeric in D67086 / R67086C4: got '25092200-sm;d'
## Warning: Expecting numeric in D67087 / R67087C4: got '25092201-sm;d'
## Warning: Expecting numeric in D67107 / R67107C4: got '8545502-sm;d'
## Warning: Expecting numeric in D67128 / R67128C4: got '2586358-me;u'
## Warning: Expecting numeric in D67129 / R67129C4: got '2586359-me;u'
## Warning: Expecting numeric in D67130 / R67130C4: got '2586360-me;u'
## Warning: Expecting numeric in D67505 / R67505C4: got '12723517-sm;u'
## Warning: Expecting numeric in D67506 / R67506C4: got '12723518-sm;u'
## Warning: Expecting numeric in D67511 / R67511C4: got '27850501-sm;d'

Analysis

DEP

Tyrosine

E vs ctrl

data_diff_E_vs_ctrl_pY <- test_diff(pY_se_Set2, type="manual", test = "E_vs_ctrl")
## Tested contrasts: E_vs_ctrl
dep_E_vs_ctrl_pY <- add_rejections_SH(data_diff_E_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_E_vs_ctrl_pY, contrast = "E_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pY") 
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Return_DEP_Hits_Plots(data = pY_Set2_form, dep_E_vs_ctrl_pY, comparison = "E_vs_ctrl_diff")
## 'select()' returned 1:many mapping between keys and columns
## Loading required namespace: reactome.db
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## character(0)
## Warning in min(screen_pval05_neg[, logFcColStr]): no non-missing arguments to
## min; returning Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

## Note: Row-scaling applied for this heatmap

PTM-SEA
GSEA_E_vs_ctrl_PTM <- Run_GSEA(DEP_result = dep_E_vs_ctrl_pY, comparison = "E_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T)
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## character(0)
GSEA_E_vs_ctrl_PTM %>% as_tibble() %>% filter(padj < 0.05) %>% arrange(desc(NES))
## # A tibble: 0 × 8
## # ℹ 8 variables: pathway <chr>, pval <dbl>, padj <dbl>, log2err <dbl>,
## #   ES <dbl>, NES <dbl>, size <int>, leadingEdge <list>
GSEA_E_vs_ctrl_PTM %>% as_tibble() %>%  arrange(desc(NES))
## # A tibble: 303 × 8
##    pathway                     pval  padj log2err    ES   NES  size leadingEdge
##    <chr>                      <dbl> <dbl>   <dbl> <dbl> <dbl> <int> <list>     
##  1 KINASE-iKiP_EPHA5        0.00397 0.816   0.407 0.988  1.47     2 <chr [2]>  
##  2 PATH-NP_AGE_RAGE_PATHWAY 0.103   0.816   0.178 0.745  1.43     6 <chr [4]>  
##  3 KINASE-iKiP_LYNB.LYN     0.153   0.816   0.135 0.615  1.33    10 <chr [4]>  
##  4 KINASE-PSP_MKK6/MAP2K6   0.111   0.816   0.180 0.833  1.33     3 <chr [3]>  
##  5 KINASE-PSP_EphA2/EPHA2   0.153   0.816   0.147 0.756  1.31     4 <chr [4]>  
##  6 PERT-PSP_H2O2            0.138   0.816   0.160 0.818  1.31     3 <chr [3]>  
##  7 KINASE-PSP_Brk/PTK6      0.0878  0.816   0.209 0.955  1.29     1 <chr [1]>  
##  8 PATH-NP_IL3_PATHWAY      0.0878  0.816   0.209 0.955  1.29     1 <chr [1]>  
##  9 KINASE-iKiP_EPHA3        0.162   0.816   0.146 0.801  1.28     3 <chr [2]>  
## 10 PERT-PSP_GDNF            0.226   0.816   0.115 0.663  1.27     6 <chr [6]>  
## # ℹ 293 more rows
Run_GSEA(DEP_result = dep_E_vs_ctrl_pY, comparison = "E_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "PATH-NP_EGFR1_PATHWAY")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## character(0)
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 66 × 4
##    HGNC_Symbol Annotated_Sequence   MOD_RSD    FC
##    <chr>       <chr>                <chr>   <dbl>
##  1 NEDD9       TGHGYVyEYPSR         Y166-p  3.75 
##  2 CTNND1      HYEDGYPGGSDNyGSLSR   Y228-p  1.68 
##  3 GAB1        DASSQDCyDIPR         Y406-p  1.54 
##  4 PXN         VGEEEHVySFPNK        Y118-p  1.29 
##  5 EPHA2       QSPEDVyFSK           Y575-p  1.29 
##  6 EPHA2       VLEDDPEATyTTSGGK     Y772-p  1.20 
##  7 EPHA2       VLEDDPEATyTTSGGKIPIR Y772-p  1.20 
##  8 PKP4        STTNyVDFYSTK         Y1168-p 0.848
##  9 PTPN11      VyENVGLMQQQK         Y580-p  0.799
## 10 PKP3        GQyHTLQAGFSSR        Y84-p   0.793
## # ℹ 56 more rows
Run_GSEA(DEP_result = dep_E_vs_ctrl_pY, comparison = "E_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_Src/SRC")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## character(0)
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 17 × 4
##    HGNC_Symbol Annotated_Sequence  MOD_RSD       FC
##    <chr>       <chr>               <chr>      <dbl>
##  1 PRKCD       TGVAGEDMQDNSGTyGK   Y334-p   1.35   
##  2 RET         DVYEEDSyVK          Y905-p   0.730  
##  3 RET         DVYEEDSyVKR         Y905-p   0.730  
##  4 CAV1        YVDSEGHLyTVPIR      Y14-p    0.539  
##  5 PRKCD       RSDSASSEPVGIyQGFEK  Y313-p   0.433  
##  6 PRKCD       RSDsASSEPVGIyQGFEK  Y313-p   0.433  
##  7 PRKCD       RSDSASSEPVGIyQGFEKK Y313-p   0.433  
##  8 PRKCD       SDSASSEPVGIyQGFEK   Y313-p   0.433  
##  9 PTK2        YMEDSTyYK           Y576-p   0.329  
## 10 STAT1       GTGyIKTELISVSEVHPSR Y701-p   0.298  
## 11 PTPRA       VVQEYIDAFSDyANFK    Y798-p   0.114  
## 12 GJA1        QASEQNWANySAEQNR    Y313-p   0.00102
## 13 ARHGAP35    NEEENIySVPHDSTQGK   Y1105-p -0.216  
## 14 CTNNB1      NEGVATyAAAVLFR      Y654-p  -0.323  
## 15 PFN1        CyEMASHLR           Y129-p  -0.332  
## 16 MPZL1       SESVVyADIR          Y263-p  -0.450  
## 17 SHC1        ELFDDPSyVNVQNLDK    Y427-p  -0.642
Run_GSEA(DEP_result = dep_E_vs_ctrl_pY, comparison = "E_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_EphA2/EPHA2")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## character(0)
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 5 × 4
##   HGNC_Symbol Annotated_Sequence   MOD_RSD    FC
##   <chr>       <chr>                <chr>   <dbl>
## 1 EPHA2       QSPEDVyFSK           Y575-p  1.29 
## 2 EPHA2       VLEDDPEATyTTSGGK     Y772-p  1.20 
## 3 EPHA2       VLEDDPEATyTTSGGKIPIR Y772-p  1.20 
## 4 EPHA2       TYVDPHTyEDPNQAVLK    Y594-p  0.383
## 5 CLDN4       SAAASNyV             Y208-p  0.311

EC vs ctrl

data_diff_EC_vs_ctrl_pY <- test_diff(pY_se_Set2, type="manual", test = "EC_vs_ctrl")
## Tested contrasts: EC_vs_ctrl
dep_EC_vs_ctrl_pY <- add_rejections_SH(data_diff_EC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pY, contrast = "EC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pY") 
Return_DEP_Hits_Plots(data = pY_Set2_form, dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

## [1] "Regulation of PTEN gene transcription"         
## [2] "Innate Immune System"                          
## [3] "Interferon Signaling"                          
## [4] "RHOG GTPase cycle"                             
## [5] "DAP12 interactions"                            
## [6] "Tie2 Signaling"                                
## [7] "Fcgamma receptor (FCGR) dependent phagocytosis"
## [8] "FCERI mediated MAPK activation"
Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", 
                               pw = "Epigenetic regulation of gene expression")
PTM-SEA
GSEA_EC_vs_ctrl_PTM <- Run_GSEA(DEP_result = dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T)
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## character(0)
GSEA_EC_vs_ctrl_PTM %>% as_tibble() %>% filter(padj < 0.05) %>% arrange(desc(NES))
## # A tibble: 0 × 8
## # ℹ 8 variables: pathway <chr>, pval <dbl>, padj <dbl>, log2err <dbl>,
## #   ES <dbl>, NES <dbl>, size <int>, leadingEdge <list>
GSEA_E_vs_ctrl_PTM %>% as_tibble() %>%  arrange(desc(NES)) %>% filter(size > 9)
## # A tibble: 7 × 8
##   pathway                    pval  padj log2err     ES    NES  size leadingEdge
##   <chr>                     <dbl> <dbl>   <dbl>  <dbl>  <dbl> <int> <list>     
## 1 KINASE-iKiP_LYNB.LYN      0.153 0.816  0.135   0.615  1.33     10 <chr [4]>  
## 2 PATH-NP_EGFR1_PATHWAY     0.142 0.816  0.122   0.424  1.25     57 <chr [20]> 
## 3 KINASE-PSP_Ret/RET        0.280 0.816  0.0920  0.502  1.18     14 <chr [9]>  
## 4 PERT-PSP_EGF              0.491 0.816  0.0649  0.456  1.01     11 <chr [4]>  
## 5 KINASE-PSP_Src/SRC        0.641 0.818  0.0510  0.378  0.871    13 <chr [6]>  
## 6 PATH-NP_TSLP_PATHWAY      0.853 0.936  0.0394  0.309  0.670    10 <chr [5]>  
## 7 PATH-NP_PROLACTIN_PATHWAY 0.246 0.816  0.186  -0.346 -1.15     25 <chr [9]>
Run_GSEA(DEP_result = dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "PATH-NP_EGFR1_PATHWAY")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## character(0)
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 66 × 4
##    HGNC_Symbol Annotated_Sequence       MOD_RSD    FC
##    <chr>       <chr>                    <chr>   <dbl>
##  1 NEDD9       TGHGYVyEYPSR             Y166-p  2.54 
##  2 PKP3        GQyHTLQAGFSSR            Y84-p   0.843
##  3 PXN         VGEEEHVySFPNK            Y118-p  0.741
##  4 GAB1        DASSQDCyDIPR             Y406-p  0.585
##  5 PKP3        ADyDTLSLR                Y176-p  0.544
##  6 ANXA1       GGPGSAVSPyPTFNPSSDVAALHK Y39-p   0.542
##  7 PEAK1       VPIVINPNAyDNLAIYK        Y635-p  0.494
##  8 CTNND1      HYEDGYPGGSDNyGSLSR       Y228-p  0.472
##  9 EPHA2       QSPEDVyFSK               Y575-p  0.413
## 10 ITGB4       DySTLTSVSSHDSR           Y1510-p 0.390
## # ℹ 56 more rows
Run_GSEA(DEP_result = dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_Src/SRC")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## character(0)
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 17 × 4
##    HGNC_Symbol Annotated_Sequence  MOD_RSD      FC
##    <chr>       <chr>               <chr>     <dbl>
##  1 RET         DVYEEDSyVK          Y905-p   0.699 
##  2 RET         DVYEEDSyVKR         Y905-p   0.699 
##  3 PRKCD       TGVAGEDMQDNSGTyGK   Y334-p   0.361 
##  4 STAT1       GTGyIKTELISVSEVHPSR Y701-p   0.196 
##  5 CAV1        YVDSEGHLyTVPIR      Y14-p    0.152 
##  6 PTK2        YMEDSTyYK           Y576-p   0.0995
##  7 GJA1        QASEQNWANySAEQNR    Y313-p  -0.134 
##  8 PFN1        CyEMASHLR           Y129-p  -0.345 
##  9 CTNNB1      NEGVATyAAAVLFR      Y654-p  -0.403 
## 10 PRKCD       RSDSASSEPVGIyQGFEK  Y313-p  -0.479 
## 11 PRKCD       RSDsASSEPVGIyQGFEK  Y313-p  -0.479 
## 12 PRKCD       RSDSASSEPVGIyQGFEKK Y313-p  -0.479 
## 13 PRKCD       SDSASSEPVGIyQGFEK   Y313-p  -0.479 
## 14 PTPRA       VVQEYIDAFSDyANFK    Y798-p  -0.645 
## 15 MPZL1       SESVVyADIR          Y263-p  -0.760 
## 16 SHC1        ELFDDPSyVNVQNLDK    Y427-p  -1.73  
## 17 ARHGAP35    NEEENIySVPHDSTQGK   Y1105-p -1.83
Run_GSEA(DEP_result = dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_EphA2/EPHA2")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## character(0)
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 5 × 4
##   HGNC_Symbol Annotated_Sequence   MOD_RSD     FC
##   <chr>       <chr>                <chr>    <dbl>
## 1 EPHA2       QSPEDVyFSK           Y575-p  0.413 
## 2 EPHA2       VLEDDPEATyTTSGGK     Y772-p  0.222 
## 3 EPHA2       VLEDDPEATyTTSGGKIPIR Y772-p  0.222 
## 4 CLDN4       SAAASNyV             Y208-p  0.0958
## 5 EPHA2       TYVDPHTyEDPNQAVLK    Y594-p  0.0727
Run_GSEA(DEP_result = dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_Ret/RET")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## character(0)
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 18 × 4
##    HGNC_Symbol Annotated_Sequence                      MOD_RSD      FC
##    <chr>       <chr>                                   <chr>     <dbl>
##  1 RET         VGPGyLGSGGSR                            Y826-p   1.50  
##  2 RET         DVYEEDSyVK                              Y905-p   0.699 
##  3 RET         DVYEEDSyVKR                             Y905-p   0.699 
##  4 RET         RRDyLDLAASTPSDSLIYDDGLSEEETPLVDCNNAPLPR Y1015-p  0.322 
##  5 MAPK14      HTDDEMTGyVATR                           Y182-p   0.206 
##  6 PTK2        YMEDSTyYK                               Y576-p   0.0995
##  7 RET         LyGMSDPNWPGESPVPLTR                     Y1062-p  0.0736
##  8 RET         ADGTNTGFPRyPNDSVYANWMLSPSAAK            Y1090-p  0.0643
##  9 RET         YPNDSVyANWMLSPSAAK                      Y1096-p -0.253 
## 10 MAPK9       TACTNFMMTPyVVTR                         Y185-p  -0.318 
## 11 RET         DVyEEDSYVK                              Y900-p  -0.398 
## 12 RET         DVyEEDSYVKR                             Y900-p  -0.398 
## 13 MAPK1       VADPDHDHTGFLTEyVATR                     Y187-p  -0.610 
## 14 MAPK1       VADPDHDHTGFLtEyVATR                     Y187-p  -0.610 
## 15 MAPK3       IADPEHDHTGFLTEyVATR                     Y204-p  -0.846 
## 16 MAPK3       IADPEHDHTGFLtEyVATR                     Y204-p  -0.846 
## 17 AFAP1L2     SSSSDEEyIYMNK                           Y54-p   -0.868 
## 18 PLCG1       NPGFyVEANPMPTFK                         Y783-p  -0.930

EBC vs ctrl

data_diff_EBC_vs_ctrl_pY <- test_diff(pY_se_Set2, type="manual", test = "EBC_vs_ctrl")
## Tested contrasts: EBC_vs_ctrl
dep_EBC_vs_ctrl_pY <- add_rejections_SH(data_diff_EBC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pY, contrast = "EBC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pY")
Return_DEP_Hits_Plots(data = pY_Set2_form, dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : There were 1 pathways for which P-values were not calculated
## properly due to unbalanced (positive and negative) gene-level statistic values.
## For such pathways pval, padj, NES, log2err are set to NA. You can try to
## increase the value of the argument nPermSimple (for example set it nPermSimple
## = 10000)

##  [1] "Signal Transduction"                           
##  [2] "Cytokine Signaling in Immune system"           
##  [3] "NPAS4 regulates expression of target genes"    
##  [4] "RET signaling"                                 
##  [5] "RAF-independent MAPK1/3 activation"            
##  [6] "Fcgamma receptor (FCGR) dependent phagocytosis"
##  [7] "Insulin receptor signalling cascade"           
##  [8] "Signaling by Erythropoietin"                   
##  [9] "GPCR downstream signalling"                    
## [10] "FCERI mediated MAPK activation"
PTM-SEA
GSEA_EBC_vs_ctrl_PTM <- Run_GSEA(DEP_result = dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T)
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "PERT-PSP_ANTI_CD3"
GSEA_EBC_vs_ctrl_PTM %>% as_tibble() %>% filter(padj < 0.05) %>% arrange(desc(NES))
## # A tibble: 1 × 8
##   pathway                 pval    padj log2err     ES   NES  size leadingEdge
##   <chr>                  <dbl>   <dbl>   <dbl>  <dbl> <dbl> <int> <list>     
## 1 PERT-PSP_ANTI_CD3 0.00000791 0.00240   0.593 -0.875 -2.02     9 <chr [8]>
Run_GSEA(DEP_result = dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "PATH-NP_EGFR1_PATHWAY")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "PERT-PSP_ANTI_CD3"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 66 × 4
##    HGNC_Symbol Annotated_Sequence       MOD_RSD      FC
##    <chr>       <chr>                    <chr>     <dbl>
##  1 ANXA1       GGPGSAVSPyPTFNPSSDVAALHK Y39-p    0.101 
##  2 NEDD9       TGHGYVyEYPSR             Y166-p   0.0854
##  3 VCL         SFLDSGyR                 Y822-p  -0.0366
##  4 ACP1        QLIIEDPyYGNDSDFETVYQQCVR Y132-p  -0.0908
##  5 PKP3        GQyHTLQAGFSSR            Y84-p   -0.103 
##  6 EPHA2       TYVDPHTyEDPNQAVLK        Y594-p  -0.225 
##  7 CDK1        IGEGTYGVVyKGR            Y19-p   -0.225 
##  8 SLC38A2     SHyADVDPENQNFLLESNLGK    Y41-p   -0.257 
##  9 SLC38A2     SHyADVDPENQNFLLESNLGKK   Y41-p   -0.257 
## 10 EFNB2       TADSVFCPHyEK             Y304-p  -0.263 
## # ℹ 56 more rows
Run_GSEA(DEP_result = dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_Src/SRC")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "PERT-PSP_ERLOTINIB" "PERT-PSP_ANTI_CD3"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 17 × 4
##    HGNC_Symbol Annotated_Sequence  MOD_RSD     FC
##    <chr>       <chr>               <chr>    <dbl>
##  1 CAV1        YVDSEGHLyTVPIR      Y14-p   -0.311
##  2 PFN1        CyEMASHLR           Y129-p  -0.406
##  3 GJA1        QASEQNWANySAEQNR    Y313-p  -0.499
##  4 STAT1       GTGyIKTELISVSEVHPSR Y701-p  -0.627
##  5 RET         DVYEEDSyVK          Y905-p  -0.808
##  6 RET         DVYEEDSyVKR         Y905-p  -0.808
##  7 PTK2        YMEDSTyYK           Y576-p  -0.866
##  8 MPZL1       SESVVyADIR          Y263-p  -0.910
##  9 PRKCD       TGVAGEDMQDNSGTyGK   Y334-p  -1.17 
## 10 CTNNB1      NEGVATyAAAVLFR      Y654-p  -1.33 
## 11 PTPRA       VVQEYIDAFSDyANFK    Y798-p  -1.42 
## 12 PRKCD       RSDSASSEPVGIyQGFEK  Y313-p  -1.52 
## 13 PRKCD       RSDsASSEPVGIyQGFEK  Y313-p  -1.52 
## 14 PRKCD       RSDSASSEPVGIyQGFEKK Y313-p  -1.52 
## 15 PRKCD       SDSASSEPVGIyQGFEK   Y313-p  -1.52 
## 16 ARHGAP35    NEEENIySVPHDSTQGK   Y1105-p -2.65 
## 17 SHC1        ELFDDPSyVNVQNLDK    Y427-p  -4.74
Run_GSEA(DEP_result = dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_EphA2/EPHA2")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "PERT-PSP_ERLOTINIB" "PERT-PSP_ANTI_CD3"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 5 × 4
##   HGNC_Symbol Annotated_Sequence   MOD_RSD     FC
##   <chr>       <chr>                <chr>    <dbl>
## 1 EPHA2       TYVDPHTyEDPNQAVLK    Y594-p  -0.225
## 2 CLDN4       SAAASNyV             Y208-p  -0.301
## 3 EPHA2       QSPEDVyFSK           Y575-p  -0.365
## 4 EPHA2       VLEDDPEATyTTSGGK     Y772-p  -0.790
## 5 EPHA2       VLEDDPEATyTTSGGKIPIR Y772-p  -0.790

EC vs E

data_diff_EC_vs_E_pY <- test_diff(pY_se_Set2, type = "manual", 
                              test = c("EC_vs_E"))
## Tested contrasts: EC_vs_E
dep_EC_vs_E_pY <- add_rejections_SH(data_diff_EC_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pY, contrast = "EC_vs_E",  add_names = TRUE, additional_title = "pY", proteins_of_interest = "EGFR")
Return_DEP_Hits_Plots(data = pY_Set2_form, dep_EC_vs_E_pY, comparison = "EC_vs_E_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## [1] "Axon guidance"                                 
## [2] "ERKs are inactivated"                          
## [3] "Generation of second messenger molecules"      
## [4] "Intracellular signaling by second messengers"  
## [5] "Fcgamma receptor (FCGR) dependent phagocytosis"
## [6] "Costimulation by the CD28 family"              
## [7] "Signaling by Receptor Tyrosine Kinases"        
## [8] "Signaling by FGFR1"                            
## [9] "Signaling by ERBB2"

## Note: Row-scaling applied for this heatmap

#data_results <- get_df_long(dep)
PTM-SEA
GSEA_EC_vs_E_PTM <- Run_GSEA(DEP_result = dep_EC_vs_E_pY, comparison = "EC_vs_E_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T)
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "PERT-PSP_ANTI_CD3"
GSEA_EC_vs_E_PTM %>% as_tibble() %>% filter(padj < 0.05) %>% arrange(desc(NES))
## # A tibble: 1 × 8
##   pathway               pval   padj log2err     ES   NES  size leadingEdge
##   <chr>                <dbl>  <dbl>   <dbl>  <dbl> <dbl> <int> <list>     
## 1 PERT-PSP_ANTI_CD3 0.000141 0.0426   0.519 -0.850 -1.84     9 <chr [7]>
Run_GSEA(DEP_result = dep_EC_vs_E_pY, comparison = "EC_vs_E_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "PATH-NP_EGFR1_PATHWAY")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## character(0)
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 66 × 4
##    HGNC_Symbol Annotated_Sequence     MOD_RSD     FC
##    <chr>       <chr>                  <chr>    <dbl>
##  1 CDK5        IGEGTyGTVFK            Y15-p   0.434 
##  2 ITSN2       LIyLVPEK               Y553-p  0.402 
##  3 PKP3        ADyDTLSLR              Y176-p  0.378 
##  4 PIK3R1      DQyLMWLTQK             Y580-p  0.311 
##  5 HIPK3       TVCSTyLQSR             Y359-p  0.238 
##  6 PRPF4B      LCDFGSASHVADNDITPyLVSR Y849-p  0.235 
##  7 DSP         GVITDQNSDGyCQTGTMSR    Y56-p   0.235 
##  8 CDK1        IGEGTYGVVyKGR          Y19-p   0.112 
##  9 VCL         SFLDSGyR               Y822-p  0.0576
## 10 PKP3        GQyHTLQAGFSSR          Y84-p   0.0496
## # ℹ 56 more rows
Run_GSEA(DEP_result = dep_EC_vs_E_pY, comparison = "EC_vs_E_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_Src/SRC")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "PERT-PSP_ANTI_CD3"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 17 × 4
##    HGNC_Symbol Annotated_Sequence  MOD_RSD      FC
##    <chr>       <chr>               <chr>     <dbl>
##  1 PFN1        CyEMASHLR           Y129-p  -0.0134
##  2 RET         DVYEEDSyVK          Y905-p  -0.0315
##  3 RET         DVYEEDSyVKR         Y905-p  -0.0315
##  4 CTNNB1      NEGVATyAAAVLFR      Y654-p  -0.0798
##  5 STAT1       GTGyIKTELISVSEVHPSR Y701-p  -0.103 
##  6 GJA1        QASEQNWANySAEQNR    Y313-p  -0.135 
##  7 PTK2        YMEDSTyYK           Y576-p  -0.229 
##  8 MPZL1       SESVVyADIR          Y263-p  -0.309 
##  9 CAV1        YVDSEGHLyTVPIR      Y14-p   -0.387 
## 10 PTPRA       VVQEYIDAFSDyANFK    Y798-p  -0.759 
## 11 PRKCD       RSDSASSEPVGIyQGFEK  Y313-p  -0.911 
## 12 PRKCD       RSDsASSEPVGIyQGFEK  Y313-p  -0.911 
## 13 PRKCD       RSDSASSEPVGIyQGFEKK Y313-p  -0.911 
## 14 PRKCD       SDSASSEPVGIyQGFEK   Y313-p  -0.911 
## 15 PRKCD       TGVAGEDMQDNSGTyGK   Y334-p  -0.987 
## 16 SHC1        ELFDDPSyVNVQNLDK    Y427-p  -1.09  
## 17 ARHGAP35    NEEENIySVPHDSTQGK   Y1105-p -1.62
Run_GSEA(DEP_result = dep_EC_vs_E_pY, comparison = "EC_vs_E_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_EphA2/EPHA2")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## [1] "PERT-PSP_ANTI_CD3"
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 5 × 4
##   HGNC_Symbol Annotated_Sequence   MOD_RSD     FC
##   <chr>       <chr>                <chr>    <dbl>
## 1 CLDN4       SAAASNyV             Y208-p  -0.216
## 2 EPHA2       TYVDPHTyEDPNQAVLK    Y594-p  -0.310
## 3 EPHA2       QSPEDVyFSK           Y575-p  -0.875
## 4 EPHA2       VLEDDPEATyTTSGGK     Y772-p  -0.978
## 5 EPHA2       VLEDDPEATyTTSGGKIPIR Y772-p  -0.978

EBC vs EC

data_diff_EBC_vs_EC_pY <- test_diff(pY_se_Set2, type = "manual", 
                              test = c("EBC_vs_EC"))
## Tested contrasts: EBC_vs_EC
dep_EBC_vs_EC_pY <- add_rejections_SH(data_diff_EBC_vs_EC_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pY, contrast = "EBC_vs_EC",  add_names = TRUE, additional_title = "pY")
Return_DEP_Hits_Plots(data = pY_Set2_form, dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff")
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## character(0)
## Warning in max(screen_pval05_pos[, logFcColStr]): no non-missing arguments to
## max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(x): no non-missing arguments to min; returning Inf
## Warning in max(x): no non-missing arguments to max; returning -Inf
## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf
## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf

## Note: Row-scaling applied for this heatmap

#data_results <- get_df_long(dep)
PTM-SEA
GSEA_EBC_vs_EC_PTM <- Run_GSEA(DEP_result = dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T)
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## character(0)
GSEA_EBC_vs_EC_PTM %>% as_tibble() %>% filter(padj < 0.05) %>% arrange(desc(NES))
## # A tibble: 0 × 8
## # ℹ 8 variables: pathway <chr>, pval <dbl>, padj <dbl>, log2err <dbl>,
## #   ES <dbl>, NES <dbl>, size <int>, leadingEdge <list>
Run_GSEA(DEP_result = dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "PATH-NP_EGFR1_PATHWAY")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## character(0)
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 66 × 4
##    HGNC_Symbol Annotated_Sequence       MOD_RSD      FC
##    <chr>       <chr>                    <chr>     <dbl>
##  1 CDK1        IGEGTYGVVyKGR            Y19-p    0.207 
##  2 VCL         SFLDSGyR                 Y822-p   0.122 
##  3 GRB2        NyVTPVNR                 Y209-p   0.101 
##  4 DYRK3       LYTyIQSR                 Y369-p  -0.0298
##  5 PFN1        CyEMASHLR                Y129-p  -0.0607
##  6 ACP1        QLIIEDPyYGNDSDFETVYQQCVR Y132-p  -0.116 
##  7 MPZL1       SESVVyADIR               Y263-p  -0.150 
##  8 EFNB2       TADSVFCPHyEK             Y304-p  -0.162 
##  9 FRK         HGHyFVALFDYQAR           Y46-p   -0.187 
## 10 DSG2        VYAPASTLVDQPyANEGTVVVTER Y979-p  -0.254 
## # ℹ 56 more rows
Run_GSEA(DEP_result = dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_Src/SRC")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## character(0)
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 17 × 4
##    HGNC_Symbol Annotated_Sequence  MOD_RSD      FC
##    <chr>       <chr>               <chr>     <dbl>
##  1 PFN1        CyEMASHLR           Y129-p  -0.0607
##  2 MPZL1       SESVVyADIR          Y263-p  -0.150 
##  3 GJA1        QASEQNWANySAEQNR    Y313-p  -0.364 
##  4 CAV1        YVDSEGHLyTVPIR      Y14-p   -0.463 
##  5 PTPRA       VVQEYIDAFSDyANFK    Y798-p  -0.773 
##  6 ARHGAP35    NEEENIySVPHDSTQGK   Y1105-p -0.818 
##  7 STAT1       GTGyIKTELISVSEVHPSR Y701-p  -0.823 
##  8 CTNNB1      NEGVATyAAAVLFR      Y654-p  -0.926 
##  9 PTK2        YMEDSTyYK           Y576-p  -0.966 
## 10 PRKCD       RSDSASSEPVGIyQGFEK  Y313-p  -1.05  
## 11 PRKCD       RSDsASSEPVGIyQGFEK  Y313-p  -1.05  
## 12 PRKCD       RSDSASSEPVGIyQGFEKK Y313-p  -1.05  
## 13 PRKCD       SDSASSEPVGIyQGFEK   Y313-p  -1.05  
## 14 RET         DVYEEDSyVK          Y905-p  -1.51  
## 15 RET         DVYEEDSyVKR         Y905-p  -1.51  
## 16 PRKCD       TGVAGEDMQDNSGTyGK   Y334-p  -1.53  
## 17 SHC1        ELFDDPSyVNVQNLDK    Y427-p  -3.01
Run_GSEA(DEP_result = dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff", return_df = T,
         ptmGSEA_site_df = all_pY_sites, PtmSigdb = PtmSigdb, ptmGSEA = T, single_pathway = "KINASE-PSP_EphA2/EPHA2")
## Joining with `by = join_by(HGNC_Symbol, Annotated_Sequence)`
## character(0)
## Joining with `by = join_by(SITE_GRP_ID)`

## # A tibble: 5 × 4
##   HGNC_Symbol Annotated_Sequence   MOD_RSD     FC
##   <chr>       <chr>                <chr>    <dbl>
## 1 EPHA2       TYVDPHTyEDPNQAVLK    Y594-p  -0.297
## 2 CLDN4       SAAASNyV             Y208-p  -0.397
## 3 EPHA2       QSPEDVyFSK           Y575-p  -0.778
## 4 EPHA2       VLEDDPEATyTTSGGK     Y772-p  -1.01 
## 5 EPHA2       VLEDDPEATyTTSGGKIPIR Y772-p  -1.01

Serine/threonine

Each condition vs ctrl

data_diff_E_vs_ctrl_pST <- test_diff(pST_se_Set2, type="manual", test = "E_vs_ctrl")
## Tested contrasts: E_vs_ctrl
dep_E_vs_ctrl_pST <- add_rejections_SH(data_diff_E_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_E_vs_ctrl_pST, contrast = "E_vs_ctrl", 
                add_names = TRUE,
                additional_title = "pST")
Return_DEP_Hits_Plots(data = pST_Set2_form, dep_E_vs_ctrl_pST, comparison = "E_vs_ctrl_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## character(0)

## Note: Row-scaling applied for this heatmap

data_diff_EC_vs_ctrl_pST <- test_diff(pST_se_Set2, type="manual", test = "EC_vs_ctrl")
## Tested contrasts: EC_vs_ctrl
dep_EC_vs_ctrl_pST <- add_rejections_SH(data_diff_EC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pST, contrast = "EC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pST") 
Return_DEP_Hits_Plots(data = pST_Set2_form, dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## character(0)

## Note: Row-scaling applied for this heatmap

Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff", 
                               pw = "Epigenetic regulation of gene expression")
data_diff_EBC_vs_ctrl_pST <- test_diff(pST_se_Set2, type="manual", test = "EBC_vs_ctrl")
## Tested contrasts: EBC_vs_ctrl
dep_EBC_vs_ctrl_pST <- add_rejections_SH(data_diff_EBC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pST, contrast = "EBC_vs_ctrl", 
                 add_names = TRUE,
                additional_title = "pST")
Return_DEP_Hits_Plots(data = pST_Set2_form, dep_EBC_vs_ctrl_pST, comparison = "EBC_vs_ctrl_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns
## Warning in fgseaMultilevel(pathways = pathways, stats = stats, minSize =
## minSize, : For some of the pathways the P-values were likely overestimated. For
## such pathways log2err is set to NA.
## [1] "Signal Transduction"                 "Innate Immune System"               
## [3] "Formation of the cornified envelope"

## Note: Row-scaling applied for this heatmap

EC vs E

data_diff_EC_vs_E_pST <- test_diff(pST_se_Set2, type = "manual", 
                              test = c("EC_vs_E"))
## Tested contrasts: EC_vs_E
dep_EC_vs_E_pST <- add_rejections_SH(data_diff_EC_vs_E_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pST, additional_title = "pST", contrast = "EC_vs_E", proteins_of_interest = "EGFR")
Return_DEP_Hits_Plots(data = pST_Set2_form, dep_EC_vs_E_pST, comparison = "EC_vs_E_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

## [1] "CDC42 GTPase cycle"                  "Cell junction organization"         
## [3] "Formation of the cornified envelope"
#data_results <- get_df_long(dep)

EBC vs EC

data_diff_EBC_vs_EC_pST <- test_diff(pST_se_Set2, type = "manual", 
                              test = c("EBC_vs_EC"))
## Tested contrasts: EBC_vs_EC
dep_EBC_vs_EC_pST <- add_rejections_SH(data_diff_EBC_vs_EC_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pST, contrast = "EBC_vs_EC",  add_names = TRUE, additional_title = "pST")
Return_DEP_Hits_Plots(data = pST_Set2_form, dep_EBC_vs_EC_pST, comparison = "EBC_vs_EC_diff")
## 'select()' returned 1:1 mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:1 mapping between keys and columns

## [1] "PTK6 Regulates Proteins Involved in RNA Processing"
## [2] "Signal Transduction"                               
## [3] "RHOG GTPase cycle"                                 
## [4] "CDC42 GTPase cycle"
#data_results <- get_df_long(dep)

Save

rowData(dep_E_vs_ctrl_pY) %>% as_tibble() %>% select(HGNC_Symbol, E_vs_ctrl_diff) %>% write.table("../Data/Kinase_enrichment/Batch1_Set2_E_vs_ctrl_pY_forstring.txt", quote = F, row.names = F, col.names = F, sep = "\t")
rowData(dep_EC_vs_ctrl_pY) %>% as_tibble() %>% select(HGNC_Symbol, EC_vs_ctrl_diff) %>% write.table("../Data/Kinase_enrichment/Batch1_Set2_EC_vs_ctrl_pY_forstring.txt", quote = F, row.names = F, col.names = F, sep = "\t")

rowData(dep_E_vs_ctrl_pY) %>% as_tibble() %>% 
  select(HGNC_Symbol, ends_with("_diff")) %>%
  group_by(HGNC_Symbol) %>%
  mutate(abs_FC = abs(E_vs_ctrl_diff) ) %>%
  arrange(desc( abs_FC) ) %>%
  slice(1) %>%
  ungroup() %>%
  select(HGNC_Symbol, ends_with("_diff") ) %>%
  write.table("../Data/Kinase_enrichment/Batch1_Set2_E_vs_ctrl_pY_mostextremeFCperprotein_forstring.txt", quote = F, row.names = F, col.names = F, sep = "\t")

rowData(dep_EC_vs_ctrl_pY) %>% as_tibble() %>% 
  select(HGNC_Symbol, ends_with("_diff")) %>%
  group_by(HGNC_Symbol) %>%
  mutate(abs_FC = abs(EC_vs_ctrl_diff) ) %>%
  arrange(desc( abs_FC) ) %>%
  slice(1) %>%
  ungroup() %>%
  select(HGNC_Symbol, ends_with("_diff") ) %>%
  write.table("../Data/Kinase_enrichment/Batch1_Set2_ECs_vs_ctrl_pY_mostextremeFCperprotein_forstring.txt", quote = F, row.names = F, col.names = F, sep = "\t")

rowData(dep_E_vs_ctrl_pY) %>% as_tibble() %>% 
  filter(E_vs_ctrl_diff>1) %>%
  select(HGNC_Symbol ) %>% unique() %>%
  write.table("../Data/Kinase_enrichment/Batch1_Set2_E_vs_ctrl_pY_FCmorethan1_forstring.txt", quote = F, row.names = F, col.names = F, sep = "\t")

rowData(dep_EC_vs_ctrl_pY) %>% as_tibble() %>% 
  filter(EC_vs_ctrl_diff>1) %>%
  select(HGNC_Symbol ) %>% unique() %>%
  write.table("../Data/Kinase_enrichment/Batch1_Set2_EC_vs_ctrl_pY_FCmorethan1_forstring.txt", quote = F, row.names = F, col.names = F, sep = "\t")

Session Info

sessionInfo()
## R version 4.2.3 (2023-03-15)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] lubridate_1.9.2             forcats_1.0.0              
##  [3] stringr_1.5.0               dplyr_1.1.2                
##  [5] purrr_1.0.2                 readr_2.1.4                
##  [7] tidyr_1.3.0                 tibble_3.2.1               
##  [9] ggplot2_3.4.2               tidyverse_2.0.0            
## [11] mdatools_0.14.0             SummarizedExperiment_1.28.0
## [13] GenomicRanges_1.50.2        GenomeInfoDb_1.34.9        
## [15] MatrixGenerics_1.10.0       matrixStats_1.0.0          
## [17] DEP_1.20.0                  org.Hs.eg.db_3.16.0        
## [19] AnnotationDbi_1.60.2        IRanges_2.32.0             
## [21] S4Vectors_0.36.2            Biobase_2.58.0             
## [23] BiocGenerics_0.44.0         fgsea_1.24.0               
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.4.3           circlize_0.4.15        fastmatch_1.1-4       
##   [4] plyr_1.8.8             igraph_1.5.1           gmm_1.8               
##   [7] lazyeval_0.2.2         shinydashboard_0.7.2   crosstalk_1.2.0       
##  [10] BiocParallel_1.32.6    digest_0.6.33          foreach_1.5.2         
##  [13] htmltools_0.5.6        fansi_1.0.4            magrittr_2.0.3        
##  [16] memoise_2.0.1          cluster_2.1.4          doParallel_1.0.17     
##  [19] tzdb_0.4.0             limma_3.54.2           ComplexHeatmap_2.14.0 
##  [22] Biostrings_2.66.0      imputeLCMD_2.1         sandwich_3.0-2        
##  [25] timechange_0.2.0       colorspace_2.1-0       blob_1.2.4            
##  [28] xfun_0.40              crayon_1.5.2           RCurl_1.98-1.12       
##  [31] jsonlite_1.8.7         impute_1.72.3          zoo_1.8-12            
##  [34] iterators_1.0.14       glue_1.6.2             hash_2.2.6.2          
##  [37] gtable_0.3.3           zlibbioc_1.44.0        XVector_0.38.0        
##  [40] GetoptLong_1.0.5       DelayedArray_0.24.0    shape_1.4.6           
##  [43] scales_1.2.1           pheatmap_1.0.12        vsn_3.66.0            
##  [46] mvtnorm_1.2-2          DBI_1.1.3              Rcpp_1.0.11           
##  [49] plotrix_3.8-2          mzR_2.32.0             viridisLite_0.4.2     
##  [52] xtable_1.8-4           clue_0.3-64            reactome.db_1.82.0    
##  [55] bit_4.0.5              preprocessCore_1.60.2  sqldf_0.4-11          
##  [58] MsCoreUtils_1.10.0     DT_0.28                htmlwidgets_1.6.2     
##  [61] httr_1.4.6             gplots_3.1.3           RColorBrewer_1.1-3    
##  [64] ellipsis_0.3.2         farver_2.1.1           pkgconfig_2.0.3       
##  [67] XML_3.99-0.14          sass_0.4.7             utf8_1.2.3            
##  [70] STRINGdb_2.10.1        labeling_0.4.2         tidyselect_1.2.0      
##  [73] rlang_1.1.1            later_1.3.1            cellranger_1.1.0      
##  [76] munsell_0.5.0          tools_4.2.3            cachem_1.0.8          
##  [79] cli_3.6.1              gsubfn_0.7             generics_0.1.3        
##  [82] RSQLite_2.3.1          fdrtool_1.2.17         evaluate_0.21         
##  [85] fastmap_1.1.1          mzID_1.36.0            yaml_2.3.7            
##  [88] knitr_1.43             bit64_4.0.5            caTools_1.18.2        
##  [91] KEGGREST_1.38.0        ncdf4_1.21             mime_0.12             
##  [94] compiler_4.2.3         rstudioapi_0.15.0      plotly_4.10.2         
##  [97] png_0.1-8              affyio_1.68.0          stringi_1.7.12        
## [100] bslib_0.5.0            highr_0.10             MSnbase_2.24.2        
## [103] lattice_0.21-8         ProtGenerics_1.30.0    Matrix_1.6-0          
## [106] tmvtnorm_1.5           vctrs_0.6.3            pillar_1.9.0          
## [109] norm_1.0-11.1          lifecycle_1.0.3        BiocManager_1.30.22   
## [112] jquerylib_0.1.4        MALDIquant_1.22.1      GlobalOptions_0.1.2   
## [115] data.table_1.14.8      cowplot_1.1.1          bitops_1.0-7          
## [118] httpuv_1.6.11          R6_2.5.1               pcaMethods_1.90.0     
## [121] affy_1.76.0            promises_1.2.1         KernSmooth_2.23-22    
## [124] codetools_0.2-19       MASS_7.3-60            gtools_3.9.4          
## [127] assertthat_0.2.1       chron_2.3-61           proto_1.0.0           
## [130] rjson_0.2.21           withr_2.5.0            GenomeInfoDbData_1.2.9
## [133] parallel_4.2.3         hms_1.1.3              grid_4.2.3            
## [136] rmarkdown_2.23         shiny_1.7.4.1
knitr::knit_exit()